PU5927: Professional Placement in Health Data Science

PU5927: PROFESSIONAL PLACEMENT IN HEALTH DATA SCIENCE (2026-2027)

Last modified: 22 Jul 2026 15:16

Course Overview

This work-based placement elective offers a professional placement with a civic, government, industrial, public, research or voluntary health and/or development sector organisation in the field of Health Data Science. You will undertake a ten-week placement with your host organisation, either within the organisation, remotely from Aberdeen, or using a combination of both. Placements are subject to availability and are offered on a best match basis.

Course Details

Study Type	Postgraduate	Level	5
Term	Third Term	Credit Points	60 credits (30 ECTS credits)
Campus	Aberdeen	Sustained Study	No
Co-ordinators	Dr Caroline Franco Mrs Toni Gibson

What courses & programmes must have been taken before this course?

PU5058 Introduction to Health Data Science (Studied)

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

PU5548 Work-Based Placement in Applied Health Sciences (Studied)
PU5923 Extended Work-Based Placement in Applied Health Science (Studied)
PU5926 Professional Placement in Health Data Science (Passed)
PU5930 Enhanced Work-Based Learning in Applied Health Sciences (Studied)

Are there a limited number of places available?

Course Description

This is a project-based course, the content and structure of which is largely student-directed. You will contribute work activities for your host organisation (40 hours per week over ten weeks = 400 hours, documented using timesheets) and teaching and learning for this course will also involve a combination of host organisation- and University-facilitated structured education (40 hours, including ten compulsory two-hour University training sessions and work-related training, plus meetings with your placement host and University supervisor). You will also spend time on self-study and preparation for assessments (160 hours).

The aims of this course are to:

Enhance student experience
Provide students with an authentic learning opportunity
Enhance student employability
Enable students to acquire professional knowledge and understanding of the daily work, strategic priorities, objectives and functions of a health and/or development sector organisation
Facilitate student application of academic, technical/programming and reproducible science skills in a work environment

Assessments are individually graded, but each of them will build on learning from previous assessments. As a result, a varied portfolio will be developed and assessed to ensure students are given continuous feedback and support to develop high quality project outputs.

Details, including assessments, may be subject to change until 21st September 2026 for Term 1 and Full Year courses and 8th January 2027 for Term 2 courses.

Summative Assessments

Oral Presentation: Individual

Assessment Type	Summative	Weighting	20
Assessment Weeks	2	Feedback Weeks		Look up Week Numbers
Feedback	Post-placement presentation.

Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Create	Create appropriate data science outputs and communicate them effectively to the relevant stakeholders.
Procedural	Evaluate	Demonstrate evidence of the application of academic and technical skills in the workplace, including collection of relevant data, synthesis, analysis, and interpretation.
Reflection	Apply	Demonstrate evidence of the use of open and reproducible science guidelines in technical analysis/outputs.

Project Plan, Summary or Abstract

Assessment Type	Summative	Weighting	10
Assessment Weeks	49	Feedback Weeks		Look up Week Numbers
Feedback	Roles and responsibilities placement agreement + risk assessment form. Up to 2000 words, including proforma text, tables or references: Placement agreement: approximately 300 words for the organisational profile; 300 words for the project plan; 300 words for roles and responsibilities; and 100 words for reflective questions. Risk assessment: around 1000 words filling a proforma table including risk assessment questions.

Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Create	Develop work placement roles and responsibilities; and negotiate these with all stakeholders.
Procedural	Evaluate	Undertake a risk assessment and prepare a formal agreement with the placement host.

Project Report/Dissertation

Assessment Type	Summative	Weighting	30
Assessment Weeks	5	Feedback Weeks		Look up Week Numbers
Feedback	Executive report on the project outputs and contributions made to the host organization.

Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Create	Create appropriate data science outputs and communicate them effectively to the relevant stakeholders.
Procedural	Evaluate	Demonstrate evidence of the application of academic and technical skills in the workplace, including collection of relevant data, synthesis, analysis, and interpretation.
Reflection	Apply	Demonstrate evidence of the use of open and reproducible science guidelines in technical analysis/outputs.

Reflective Report

Assessment Type	Summative	Weighting	10
Assessment Weeks	2	Feedback Weeks		Look up Week Numbers
Feedback	Post-placement presentation – B. Oral reflective report on skills gained and contributions made during the placement.

Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Reflection	Evaluate	Critically evaluate and describe the work of a health data scientist as it is situated within the broader context of everyday life.

Oral Presentation: Individual

Assessment Type	Summative	Weighting	10
Assessment Weeks	49	Feedback Weeks		Look up Week Numbers
Feedback	Pre-placement presentation (synchronous)

Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Create	Design an efficient project workflow.

GitHub Repository

Assessment Type	Summative	Weighting	20
Assessment Weeks	4	Feedback Weeks		Look up Week Numbers
Feedback	GitHub repository showing continued use of open and reproducible science framework.

Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Apply	Operate collaborative science platforms.

Formative Assessment

There are no assessments for this course.

Resit Assessments

Report: Individual

Assessment Type	Summative	Weighting	60
Assessment Weeks	4	Feedback Weeks	6	Look up Week Numbers
Feedback	The written report will consist on a short health data science project (1500-2000 words), and will require the application of the open and reproducible science framework to address and example healthcare problem.

Learning Outcomes

Sorry, we don't have this information available just now. Please check the course guide on or with the Course Coordinator
Knowledge Level	Thinking Skill	Outcome

Oral Exam

Assessment Type	Summative	Weighting	40
Assessment Weeks	4	Feedback Weeks	6	Look up Week Numbers
Feedback	The oral examination will last approximately 30 minutes, which will include a presentation of approximately 15 minutes.

Learning Outcomes

Sorry, we don't have this information available just now. Please check the course guide on or with the Course Coordinator
Knowledge Level	Thinking Skill	Outcome

Course Learning Outcomes

Knowledge Level	Thinking Skill	Outcome
Procedural	Evaluate	Undertake a risk assessment and prepare a formal agreement with the placement host.
Procedural	Create	Develop work placement roles and responsibilities; and negotiate these with all stakeholders.
Reflection	Apply	Demonstrate evidence of the use of open and reproducible science guidelines in technical analysis/outputs.
Reflection	Evaluate	Critically evaluate and describe the work of a health data scientist as it is situated within the broader context of everyday life.
Procedural	Apply	Operate collaborative science platforms.
Procedural	Evaluate	Demonstrate evidence of the application of academic and technical skills in the workplace, including collection of relevant data, synthesis, analysis, and interpretation.
Procedural	Create	Create appropriate data science outputs and communicate them effectively to the relevant stakeholders.
Procedural	Create	Design an efficient project workflow.

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